Reliability analysis of unsaturated soil slope stability using spatial random field-based Bayesian method

Rainfall and water level change are two of the main factors causing failure of reservoir slopes. Thus, soil-fluid mechanics is applied with the fluctuation of groundwater table. However, many of the geotechnical parameters needed for the analysis are highly varied. Spatial random field-based Bayesia...

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Published inLandslides Vol. 18; no. 3; pp. 1177 - 1189
Main Authors Huang, M. L., Sun, D. A., Wang, C. H., Keleta, Y.
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2021
Springer Nature B.V
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Abstract Rainfall and water level change are two of the main factors causing failure of reservoir slopes. Thus, soil-fluid mechanics is applied with the fluctuation of groundwater table. However, many of the geotechnical parameters needed for the analysis are highly varied. Spatial random field-based Bayesian method is proposed, which can systematically assimilate prior knowledge, borehole testing data, and long-term monitoring data to obtain posterior distributions. There are three major components of the method. They include unsaturated soil-fluid coupling mechanics, spatial multivariate discretization, and subset Monte Carlo simulation of reliability analysis. The approach is applied to the Shiliushubao slope of the Three Gorges Reservoir area, which is located 9.0 km downstream of the Badong County in Hubei Province, China. Results prove that the updated key geotechnical parameters will quantitatively predict the geohazards of landslide, especially for unsaturated soil slope conditions that suffer an unprecedented heavy rainfall subject to low reservoir water level in the upcoming summer.
AbstractList Rainfall and water level change are two of the main factors causing failure of reservoir slopes. Thus, soil-fluid mechanics is applied with the fluctuation of groundwater table. However, many of the geotechnical parameters needed for the analysis are highly varied. Spatial random field-based Bayesian method is proposed, which can systematically assimilate prior knowledge, borehole testing data, and long-term monitoring data to obtain posterior distributions. There are three major components of the method. They include unsaturated soil-fluid coupling mechanics, spatial multivariate discretization, and subset Monte Carlo simulation of reliability analysis. The approach is applied to the Shiliushubao slope of the Three Gorges Reservoir area, which is located 9.0 km downstream of the Badong County in Hubei Province, China. Results prove that the updated key geotechnical parameters will quantitatively predict the geohazards of landslide, especially for unsaturated soil slope conditions that suffer an unprecedented heavy rainfall subject to low reservoir water level in the upcoming summer.
Author Huang, M. L.
Sun, D. A.
Wang, C. H.
Keleta, Y.
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  fullname: Sun, D. A.
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  fullname: Wang, C. H.
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  organization: Department of Civil Engineering, Shanghai University
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  surname: Keleta
  fullname: Keleta, Y.
  organization: School of Advanced Studies, University of Phoenix
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Keywords Geotechnical parameters
Reliability analysis
Unsaturated soil slope
Spatial random fields
Bayesian method
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SSID ssj0029039
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Snippet Rainfall and water level change are two of the main factors causing failure of reservoir slopes. Thus, soil-fluid mechanics is applied with the fluctuation of...
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SubjectTerms Agriculture
Bayesian analysis
Boreholes
Canyons
Civil Engineering
Downstream effects
Earth and Environmental Science
Earth Sciences
Fields (mathematics)
Fluid mechanics
Geography
Geological hazards
Groundwater
Groundwater levels
Groundwater table
Heavy rainfall
Landslides
Monte Carlo simulation
Natural Hazards
Parameters
Probability theory
Rain
Rainfall
Reliability
Reliability analysis
Reservoir water
Reservoirs
Slope stability
Soil analysis
Soil conditions
Soil mechanics
Soil stability
Stability analysis
Statistical methods
Technical Note
Unsaturated soils
Water level fluctuations
Water levels
Water table
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Title Reliability analysis of unsaturated soil slope stability using spatial random field-based Bayesian method
URI https://link.springer.com/article/10.1007/s10346-020-01525-0
https://www.proquest.com/docview/2497619926
Volume 18
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